...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Building and environment >Non-intrusive comfort sensing: Detecting age and gender from infrared images for personal thermal comfort
【24h】

Non-intrusive comfort sensing: Detecting age and gender from infrared images for personal thermal comfort

机译：Non-intrusive comfort sensing: Detecting age and gender from infrared images for personal thermal comfort

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

The assurance of personal thermal comfort is a challenging and significant issue for achieving energy savings in buildings. As age, gender, and human skin temperature have all been proved to be correlated to thermal comfort, we propose a new non-intrusive method of predicting personal thermal comfort by extracting these three pa-rameters from thermal images. This study focuses on one key step of the method, recognizing age and gender from thermal images. We established a dataset of 3000 thermal images and 3000 visible-light images with a balanced distribution of age and gender and conducted three pre-processing experiments regarding thermal image processing, sampling strategy, and data augmentation. Then, we applied four convolutional neural net-works (CNN) to detect age and gender from thermal images and visible-light images, respectively. Finally, we used class activation mapping (CAM) to understand how CNN recognizes age and gender from thermal images. ResNet-50 achieved a gender accuracy of 95.3%, while EfficientNet provided a one-off age accuracy of 73.1%. These accuracy rates are higher than or comparable to those in the existing literature. Moreover, thermal images performed better than visible-light pictures as they offered similar classification accuracy, required less training time, and provided skin temperatures that visible-light images cannot provide CNN for learning. Besides, image view distance and view angle did not influence classification accuracy, provided some crucial facial features were exposed to the thermal cameras. This study demonstrates the feasibility of using CNN to detect age and gender from thermal images, laying a reliable foundation for the proposed automatic prediction of personal thermal comfort.

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号